Soluble and Cellular Inflammatory Predictive Markers Associated with Recurrent Pregnancy Loss Among Kazakhstani Women: a Pilot Study
Bibliographic record
Abstract
Background: Recurrent pregnancy loss (RPL) is a common complication of pregnancy globally, characterized by multiple miscarriages but with poorly explained etiologies. Insofar as a state of low-grade inflammation (LGI) accompanies RPL, this study explores the link between RPL and markers of LGI among Kazakhstani women. Methods: The retrospective study was conducted on 112 Kazakh women, comprising 64 with a confirmed diagnosis of RPL and 48 women with two or more uncomplicated pregnancies serving as controls. Statistical analysis was performed on SPSS 29 software. Results: All tested blood analytes, including CRP, glucose, cholesterol, LDL-cholesterol, Hemoglobin, and RBC counts, were negatively associated with RPL. The only exception was neutrophil values having a positive association with RPL despite a lack of significant correlation between groups. Conclusion: The study shows a marginal association between the LGI biomarkers considered and the overall risk factors of RPL in Kazakh women, which is in apparent contradiction with earlier studies. The absence of parallel studies in Central Asian countries hampers the analysis of study trends in related communities. Future case-control studies with more sample sizes are needed to explore the RPL biomarkers in depth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".